The Arbitrary Lagrangian–Eulerian (ALE) method is well established in finite element (FE) modeling and simulation of manufacturing processes due to its ability to handle large workpiece deformations more robustly than purely Lagrangian formulations. It is therefore commonly employed in scenarios where Lagrangian models fail due to severe element distortion and where Eulerian models struggle to accurately represent free workpiece surfaces and contact with tools. Beyond enabling such simulations in the first place, the ALE framework also provides substantial model-reduction benefits. A key feature of ALE-based modeling is the definition of a control volume through which material may flow at selected Eulerian boundaries, while remaining consistent with free material surfaces or interfaces. The nature and complexity of this control volume depend strongly on the manufacturing process under consideration. For example, in rolling simulations, the control volume is typically attached to the rolling stand and includes the deformation zone, bounded by Eulerian in- and outflow boundaries on the sheet material. In contrast, in advanced milling simulations, the control volume is attached to the tool's cutting edge and moves through space, sweeping over the workpiece material. This approach requires kinematic ALE mesh constraints. In rolling, material flows through the control volume, whereas in milling, the control volume itself traverses the material. A major drawback of this established modeling strategy is that it captures only the current process zone. Once material leaves the control volume, its state is no longer tracked, which is particularly limiting for transient processes. This work addresses this limitation by proposing a reconstruction approach based on the time-dependent boundary data of the control volume. The output of an existing ALE-based parent model is used to construct a secondary model that reconstructs the geometry and selected field state variables, such as temperature, downstream of the control volume. The proposed approach is demonstrated by augmenting an already developed two-dimensional chip-formation milling process model and a two-dimensional hot-rolling model.